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Introducing WorkLens: Quality Intelligence for Human and AI Work
By Pirouz5 min read
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Most organisations check a fraction of the work they produce, because reading all of it was never possible. WorkLens reads every piece of work, scores it against the standard you set, and shows you the evidence behind every score. Whether a person or an AI did the work.
Ask a service director how good their team's work is and you will get an honest answer with an uncomfortable footnote. They know, roughly, from the small sample somebody managed to review this month. Traditional quality assurance covers 1 to 3% of customer interactions, because reading more than that by hand was never realistic.
That was a tolerable compromise when the work was done by people at human speed. It is not tolerable now. Volumes are rising, AI agents are taking on a growing share of the work, and the questions being asked about quality are getting sharper, from customers, from boards and increasingly from regulators.
WorkLens is our answer. It reads every piece of work your organisation produces, judges it against the standard you define, and tells you how good it was and why.
WorkLens is a quality intelligence platform. You tell it what good work looks like in your organisation. It then reviews everything that comes in against that standard, scores it across the dimensions you care about, and hands your team leads a clear picture of where quality is strong and where it needs attention.
It does not replace the judgment of the people running your teams. It removes the part of their job that was never possible in the first place, which is reading everything, and leaves them the part that matters, which is deciding what to do about it.
The first two steps belong to you, and that is deliberate. Quality is specific to your business, your customers and your brand, so the standard has to be yours rather than ours.
You define your quality criteria, per team, channel or product. You point WorkLens at real examples of work you are proud of, because good examples teach a standard far better than a written rule ever does. From there, work flows in automatically from the systems you already use, and every item gets reviewed.
Coverage stops being a compromise. Moving from a small sample to complete coverage changes what quality data can be used for. A number based on 2% of interactions supports an opinion. A number based on all of them supports a decision.
Everyone is measured the same way. Manual review varies by reviewer, by mood and by how much time was left in the week. One standard, applied consistently, makes comparisons between people and teams fair enough to act on.
Coaching gets specific. Instead of general advice in a quarterly conversation, a team lead can point at the exact moment in a real interaction where something went well or went wrong, with the reasoning already written up.
Problems surface while they still matter. Issues appear as they happen rather than in a report three weeks later, by which time the customer has already formed a view.
Pirouz
Salesforce Consultant
This is the part that will matter most over the next few years. Gartner expects that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention, reducing operational costs by 30%.
If that is even approximately right, most organisations are about to hand a large share of their customer-facing work to systems they have no established way of assessing. The quality function was built to review people. It now needs to review machines too, on the same terms, and produce evidence that stands up when somebody asks.
WorkLens treats an AI agent as it treats a person. Same work item, same standard, same score, same evidence. Quality stays one conversation rather than splitting into two disconnected ones.
Quality is not a customer service problem. It is a question you can ask about any piece of work. Was this contract sound? Was this invoice handled correctly? Was this assignment marked fairly? Was this deal actually run properly?
WorkLens for Service is available today. Sales is next, and legal, finance and education follow. Because every module runs on the same platform, adding the second one is a configuration exercise rather than a second implementation project.
One idea holds the platform together, and it is worth understanding because it explains why the modules work the way they do.
Everything that enters WorkLens is treated as a work item. A service case is a work item. So is a sales opportunity, a contract, an invoice, a submitted assignment. The content is completely different, but the treatment is identical: compare it against the standard, score it, explain the score.
That is why the platform can move from a contact centre into a legal department without being rebuilt. The engine does not care what kind of work it is reading. It cares what standard it is reading against.
A score nobody trusts is worse than no score at all, so every result in WorkLens comes with its reasoning attached.
Your team lead sees the score, the dimensions behind it, the specific evidence that produced it, and a clear statement of what was missing. If they disagree, they can correct it, and the correction is part of the record. Oversight is not a claim we make on the platform's behalf. It is something your people do, and something you can show.
WorkLens is hosted in Europe, with control over what is retained and for how long. Access is role-based and every action leaves an audit trail. It connects to the systems your work already lives in, including Salesforce, HubSpot, Pipedrive and Zoho, alongside mail and telephony.
For organisations preparing for obligations under the EU AI Act, the ability to demonstrate that a person can interpret an AI system's output and override it is not a nice-to-have. It is written into the regulation. WorkLens is built so that evidence exists by default rather than being assembled after the fact.
Most organisations start with the area where the pressure is highest, which is usually customer service, and expand once the standard is proven and the team trusts the scores.
If you want to see what WorkLens makes of your own work, we can walk you through it on your data rather than a demo dataset. That conversation is usually the fastest way to find out whether your quality standard is as clear as you think it is.